Ultrasound imaging systems detect and display information relating to Doppler shifts of returning echoes. The Doppler shifts represent movement, such as blood flow patterns. Spectral Doppler is one known ultrasound technique to detect and display blood flow patterns associated with a point or points in a body.
In spectral Doppler, an ongoing series of Doppler spectra are displayed for a particular point in the body. Each spectrum represents a point in time and a range of frequencies and associated signal strengths. The frequencies are proportional to blood flow velocities. The signal strength of each frequency is displayed on the spectrum as a brightness.
The spectrum is estimated by a signal processor. An ongoing stream of Doppler data samples is provided to the signal processor. The signal processor typically implements one of two methods for estimating spectra from the Doppler data.
One method to estimate each spectrum applies a conventional short-time Fourier transform (STFT) algorithm. In particular, a block of ongoing Doppler data is multiplied by a sliding window function. The Discrete Fourier Transform (DFT) of the output of the window function is computed. Generally, the DFT is based on a certain size or number of sample points, such as a 128 point DFT. The window function and the DFT function output the same number of sample points as are input. If the spectrum is displayed with more samples, then the samples are interpolated from the output of the DFT calculation.
The next spectrum is estimated using the same method on a different block of Doppler data. Generally, each spectrum is of the same size, such as a multiple point (128) spectrum. The different block of data is obtained by sliding or positioning the window function to operate on a block of Doppler data that includes at least one Doppler data sample obtained after the samples in the previous block of Doppler data. The sliding window is positioned based on a generally fixed time interval between each successive window.
The number of Doppler data samples used for calculating the spectrum varies based on the pulse repetition interval (PRI). The length of the window function, which determines the number of samples included in the block of Doppler samples, is varied between 32 and 128 samples as a function of the PRI. Varying the length of the window function prevents temporal resolution degradation as the PRI increases. Generally, the length of the window function is decreased as the PRI increases.
There are disadvantages to decreasing the length of the window. When the shorter window functions are used, data is interpolated from the estimated spectrum in order to output a certain size spectrum, such as a 128 point spectrum. By decreasing the window function size, the frequency resolution of the spectra is decreased. For example, a spectrum computed using a 32 sample window function has one quarter the frequency resolution of a spectrum computed using a 128 sample window function.
The second method to estimate each spectrum uses an autoregressive model. The ongoing Doppler data samples are modeled with an autoregressive model characterized by a set of autoregressive parameters. Using a z-transform, a spectral estimate is produced by computing the spectrum that corresponds to the autoregressive model. The autoregressive model generates spectra with improved frequency resolution as compared to the STFT based method described above. However, autoregressive models: (1) produce spectra with higher dynamic range than the conventional STFT method, (2) may not include spectral features due to poor approximation of the Doppler data, and (3) produce spectral strips with a different look than strips produced by conventional STFT methods.
Neither of the ultrasound systems and methods for estimating spectra is entirely satisfactory. It is therefore desirable to provide a method and system for enhanced-resolution spectral Doppler display.